# Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents Source: [Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:41:29.853Z Authors: Kelly Lais Wiggers, Alceu de Souza Britto Junior, Alessandro Lameiras Koerich, Laurent Heutte, Luiz Eduardo Soares de Oliveira Published: 2019-07-22T16:27:19.000Z ## Abstract This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning. The first approach uses a pre-trained CNN model to cope with the lack of training data, which is fine-tuned to achieve a compact yet discriminant representation of queries and image candidates. The second approach uses a Siamese Convolution Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset to provide the similarity-based feature maps. In both methods, the learned representation scheme considers feature maps of different sizes which are evaluated in terms of retrieval performance. A robust experimental protocol using two public datasets (Tobacoo-800 and DocExplore) has shown that the proposed methods compare favorably against state-of-the-art document image retrieval and pattern spotting methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()